






"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


snake_case : Any     		=		{
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def 							A						(				__snake_case: List[Any]  )    ->		int:

       """simple docstring"""





       assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
       assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
       if args.mlm:
              assert os.path.isfile(args.token_counts  )
              assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
       else:
              assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

       assert args.teacher_type == args.student_type or (
           args.student_type == "distilbert" and args.teacher_type == "bert"
       )
       assert os.path.isfile(args.student_config  )
       if args.student_pretrained_weights is not None:
              assert os.path.isfile(args.student_pretrained_weights  )

       if args.freeze_token_type_embds:
              assert args.student_type in ["roberta"]

       assert args.alpha_ce >= 0.0
       assert args.alpha_mlm >= 0.0
       assert args.alpha_clm >= 0.0
       assert args.alpha_mse >= 0.0
       assert args.alpha_cos >= 0.0
       assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def 							A						(				__snake_case: Union[str, Any] ,     __snake_case: Union[str, Any]  )    ->		Union[str, Any]:

       """simple docstring"""





       if args.student_type == "roberta":
              __magic_name__      				=    False
       elif args.student_type == "gpt2":
              __magic_name__      				=    False


def 							A						(				__snake_case: int ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       if args.student_type == "roberta":
              __magic_name__      				=    False


def 							A						(				)    ->		Union[str, Any]:

       """simple docstring"""





       __magic_name__      				=    argparse.ArgumentParser(description='Training'  )
       parser.add_argument('--force' ,     action='store_true' ,     help='Overwrite dump_path if it already exists.'  )

       parser.add_argument(
           '--dump_path' ,     type=__snake_case ,     required=__snake_case ,     help='The output directory (log, checkpoints, parameters, etc.)'  )
       parser.add_argument(
           '--data_file' ,     type=__snake_case ,     required=__snake_case ,     help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.' ,     )

       parser.add_argument(
           '--student_type' ,     type=__snake_case ,     choices=['distilbert', 'roberta', 'gpt2'] ,     required=__snake_case ,     help='The student type (DistilBERT, RoBERTa).' ,     )
       parser.add_argument('--student_config' ,     type=__snake_case ,     required=__snake_case ,     help='Path to the student configuration.'  )
       parser.add_argument(
           '--student_pretrained_weights' ,     default=__snake_case ,     type=__snake_case ,     help='Load student initialization checkpoint.'  )

       parser.add_argument(
           '--teacher_type' ,     choices=['bert', 'roberta', 'gpt2'] ,     required=__snake_case ,     help='Teacher type (BERT, RoBERTa).'  )
       parser.add_argument('--teacher_name' ,     type=__snake_case ,     required=__snake_case ,     help='The teacher model.'  )

       parser.add_argument('--temperature' ,     default=2.0 ,     type=__snake_case ,     help='Temperature for the softmax temperature.'  )
       parser.add_argument(
           '--alpha_ce' ,     default=0.5 ,     type=__snake_case ,     help='Linear weight for the distillation loss. Must be >=0.'  )
       parser.add_argument(
           '--alpha_mlm' ,     default=0.0 ,     type=__snake_case ,     help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.' ,     )
       parser.add_argument('--alpha_clm' ,     default=0.5 ,     type=__snake_case ,     help='Linear weight for the CLM loss. Must be >=0.'  )
       parser.add_argument('--alpha_mse' ,     default=0.0 ,     type=__snake_case ,     help='Linear weight of the MSE loss. Must be >=0.'  )
       parser.add_argument(
           '--alpha_cos' ,     default=0.0 ,     type=__snake_case ,     help='Linear weight of the cosine embedding loss. Must be >=0.'  )

       parser.add_argument(
           '--mlm' ,     action='store_true' ,     help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'  )
       parser.add_argument(
           '--mlm_mask_prop' ,     default=0.15 ,     type=__snake_case ,     help='Proportion of tokens for which we need to make a prediction.' ,     )
       parser.add_argument('--word_mask' ,     default=0.8 ,     type=__snake_case ,     help='Proportion of tokens to mask out.'  )
       parser.add_argument('--word_keep' ,     default=0.1 ,     type=__snake_case ,     help='Proportion of tokens to keep.'  )
       parser.add_argument('--word_rand' ,     default=0.1 ,     type=__snake_case ,     help='Proportion of tokens to randomly replace.'  )
       parser.add_argument(
           '--mlm_smoothing' ,     default=0.7 ,     type=__snake_case ,     help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).' ,     )
       parser.add_argument('--token_counts' ,     type=__snake_case ,     help='The token counts in the data_file for MLM.'  )

       parser.add_argument(
           '--restrict_ce_to_mask' ,     action='store_true' ,     help='If true, compute the distillation loss only the [MLM] prediction distribution.' ,     )
       parser.add_argument(
           '--freeze_pos_embs' ,     action='store_true' ,     help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.' ,     )
       parser.add_argument(
           '--freeze_token_type_embds' ,     action='store_true' ,     help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.' ,     )

       parser.add_argument('--n_epoch' ,     type=__snake_case ,     default=3 ,     help='Number of pass on the whole dataset.'  )
       parser.add_argument('--batch_size' ,     type=__snake_case ,     default=5 ,     help='Batch size (for each process).'  )
       parser.add_argument(
           '--group_by_size' ,     action='store_false' ,     help='If true, group sequences that have similar length into the same batch. Default is true.' ,     )

       parser.add_argument(
           '--gradient_accumulation_steps' ,     type=__snake_case ,     default=5_0 ,     help='Gradient accumulation for larger training batches.' ,     )
       parser.add_argument('--warmup_prop' ,     default=0.05 ,     type=__snake_case ,     help='Linear warmup proportion.'  )
       parser.add_argument('--weight_decay' ,     default=0.0 ,     type=__snake_case ,     help='Weight decay if we apply some.'  )
       parser.add_argument('--learning_rate' ,     default=5E-4 ,     type=__snake_case ,     help='The initial learning rate for Adam.'  )
       parser.add_argument('--adam_epsilon' ,     default=1E-6 ,     type=__snake_case ,     help='Epsilon for Adam optimizer.'  )
       parser.add_argument('--max_grad_norm' ,     default=5.0 ,     type=__snake_case ,     help='Max gradient norm.'  )
       parser.add_argument('--initializer_range' ,     default=0.02 ,     type=__snake_case ,     help='Random initialization range.'  )

       parser.add_argument(
           '--fp16' ,     action='store_true' ,     help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit' ,     )
       parser.add_argument(
           '--fp16_opt_level' ,     type=__snake_case ,     default='O1' ,     help=(
               'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
               'See details at https://nvidia.github.io/apex/amp.html'
           ) ,     )
       parser.add_argument('--n_gpu' ,     type=__snake_case ,     default=1 ,     help='Number of GPUs in the node.'  )
       parser.add_argument('--local_rank' ,     type=__snake_case ,     default=-1 ,     help='Distributed training - Local rank'  )
       parser.add_argument('--seed' ,     type=__snake_case ,     default=5_6 ,     help='Random seed'  )

       parser.add_argument('--log_interval' ,     type=__snake_case ,     default=5_0_0 ,     help='Tensorboard logging interval.'  )
       parser.add_argument('--checkpoint_interval' ,     type=__snake_case ,     default=4_0_0_0 ,     help='Checkpoint interval.'  )
       __magic_name__      				=    parser.parse_args()
       sanity_checks(__snake_case  )

       # ARGS #
       init_gpu_params(__snake_case  )
       set_seed(__snake_case  )
       if args.is_master:
              if os.path.exists(args.dump_path  ):
                     if not args.force:
                            raise ValueError(
                                F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
                                ' itUse `--force` if you want to overwrite it'  )
                     else:
                            shutil.rmtree(args.dump_path  )

              if not os.path.exists(args.dump_path  ):
                     os.makedirs(args.dump_path  )
              logger.info(F"""Experiment will be dumped and logged in {args.dump_path}"""  )

              # SAVE PARAMS #
              logger.info(F"""Param: {args}"""  )
              with open(os.path.join(args.dump_path ,     'parameters.json'  ) ,     'w'  ) as f:
                     json.dump(vars(__snake_case  ) ,     __snake_case ,     indent=4  )
              git_log(args.dump_path  )

       __magic_name__				,  __magic_name__				,  __magic_name__      				=    MODEL_CLASSES[args.student_type]
       __magic_name__				,  __magic_name__				,  __magic_name__      				=    MODEL_CLASSES[args.teacher_type]

       # TOKENIZER #
       __magic_name__      				=    teacher_tokenizer_class.from_pretrained(args.teacher_name  )
       __magic_name__      				=    {}
       for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
              __magic_name__      				=    tokenizer.all_special_tokens.index(__snake_case  )
              __magic_name__      				=    tokenizer.all_special_ids[idx]
       logger.info(F"""Special tokens {special_tok_ids}"""  )
       __magic_name__      				=    special_tok_ids
       __magic_name__      				=    tokenizer.max_model_input_sizes[args.teacher_name]

       # DATA LOADER #
       logger.info(F"""Loading data from {args.data_file}"""  )
       with open(args.data_file ,     'rb'  ) as fp:
              __magic_name__      				=    pickle.load(__snake_case  )

       if args.mlm:
              logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""  )
              with open(args.token_counts ,     'rb'  ) as fp:
                     __magic_name__      				=    pickle.load(__snake_case  )

              __magic_name__      				=    np.maximum(__snake_case ,     1  ) ** -args.mlm_smoothing
              for idx in special_tok_ids.values():
                     __magic_name__      				=    0.0  # do not predict special tokens
              __magic_name__      				=    torch.from_numpy(__snake_case  )
       else:
              __magic_name__      				=    None

       __magic_name__      				=    LmSeqsDataset(params=__snake_case ,     data=__snake_case  )
       logger.info('Data loader created.'  )

       # STUDENT #
       logger.info(F"""Loading student config from {args.student_config}"""  )
       __magic_name__      				=    student_config_class.from_pretrained(args.student_config  )
       __magic_name__      				=    True

       if args.student_pretrained_weights is not None:
              logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
              __magic_name__      				=    student_model_class.from_pretrained(args.student_pretrained_weights ,     config=__snake_case  )
       else:
              __magic_name__      				=    student_model_class(__snake_case  )

       if args.n_gpu > 0:
              student.to(F"""cuda:{args.local_rank}"""  )
       logger.info('Student loaded.'  )

       # TEACHER #
       __magic_name__      				=    teacher_model_class.from_pretrained(args.teacher_name ,     output_hidden_states=__snake_case  )
       if args.n_gpu > 0:
              teacher.to(F"""cuda:{args.local_rank}"""  )
       logger.info(F"""Teacher loaded from {args.teacher_name}."""  )

       # FREEZING #
       if args.freeze_pos_embs:
              freeze_pos_embeddings(__snake_case ,     __snake_case  )
       if args.freeze_token_type_embds:
              freeze_token_type_embeddings(__snake_case ,     __snake_case  )

       # SANITY CHECKS #
       assert student.config.vocab_size == teacher.config.vocab_size
       assert student.config.hidden_size == teacher.config.hidden_size
       assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
       if args.mlm:
              assert token_probs.size(0  ) == stu_architecture_config.vocab_size

       # DISTILLER #
       torch.cuda.empty_cache()
       __magic_name__      				=    Distiller(
           params=__snake_case ,     dataset=__snake_case ,     token_probs=__snake_case ,     student=__snake_case ,     teacher=__snake_case  )
       distiller.train()
       logger.info('Let\'s go get some drinks.'  )


if __name__ == "__main__":
 main()







"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


snake_case : Any     		=		logging.getLogger(__name__)
snake_case : Optional[Any]     		=		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case : Optional[Any]     		=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a_)}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}							,	)
    __UpperCAmelCase           = field(
        default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }							,	)







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
                  raise ValueError(
                      '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path' )



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """The input training data file (a text file)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
    __UpperCAmelCase           = field(
        default=5							,	metadata={
            """help""": """The percentage of the train set used as validation set in case there's no validation split"""
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated. Default to the max input length of the model."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The number of processes to use for the preprocessing."""}							,	)
    __UpperCAmelCase           = field(
        default=0.15							,	metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }							,	)







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           if self.train_file is not None:
                  __magic_name__      				=    self.train_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
           if self.validation_file is not None:
                  __magic_name__      				=    self.validation_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def 							A						(				__snake_case: Tuple ,     __snake_case: str  )    ->		Tuple:

       """simple docstring"""





       with open(__snake_case ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.loads(__snake_case  ) for line in f.read().splitlines() if (len(__snake_case  ) > 0 and not line.isspace())]
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    {c: dataset[c] for c in dataset.column_names}
       __magic_name__      				=    refs
       return Dataset.from_dict(__snake_case  )


def 							A						(				)    ->		Union[str, Any]:

       """simple docstring"""





       __magic_name__      				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
       if len(sys.argv  ) == 2 and sys.argv[1].endswith('.json'  ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
       else:
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       __magic_name__      				=    None
       if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
              __magic_name__      				=    get_last_checkpoint(training_args.output_dir  )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
                     raise ValueError(
                         F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         'Use --overwrite_output_dir to overcome.'  )
              elif last_checkpoint is not None:
                     logger.info(
                         F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,     datefmt='%m/%d/%Y %H:%M:%S' ,     handlers=[logging.StreamHandler(sys.stdout  )] ,     )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

       # Log on each process the small summary:
       logger.warning(
           F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + F"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank  ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('Training/evaluation parameters %s' ,     __snake_case  )

       # Set seed before initializing model.
       set_seed(training_args.seed  )

       # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
       # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
       # (the dataset will be downloaded automatically from the datasets Hub).
       #
       # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
       # 'text' is found. You can easily tweak this behavior (see below).
       #
       # In distributed training, the load_dataset function guarantee that only one local process can concurrently
       # download the dataset.
       if data_args.dataset_name is not None:
              # Downloading and loading a dataset from the hub.
              __magic_name__      				=    load_dataset(data_args.dataset_name ,     data_args.dataset_config_name  )
              if "validation" not in datasets.keys():
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[:{data_args.validation_split_percentage}%]""" ,     )
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[{data_args.validation_split_percentage}%:]""" ,     )
       else:
              __magic_name__      				=    {}
              if data_args.train_file is not None:
                     __magic_name__      				=    data_args.train_file
              if data_args.validation_file is not None:
                     __magic_name__      				=    data_args.validation_file
              __magic_name__      				=    data_args.train_file.split('.'  )[-1]
              if extension == "txt":
                     __magic_name__      				=    'text'
              __magic_name__      				=    load_dataset(__snake_case ,     data_files=__snake_case  )
       # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
       # https://huggingface.co/docs/datasets/loading_datasets.html.

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.config_name:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.config_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              __magic_name__      				=    CONFIG_MAPPING[model_args.model_type]()
              logger.warning('You are instantiating a new config instance from scratch.'  )
              if model_args.config_overrides is not None:
                     logger.info(F"""Overriding config: {model_args.config_overrides}"""  )
                     config.update_from_string(model_args.config_overrides  )
                     logger.info(F"""New config: {config}"""  )

       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'use_fast': model_args.use_fast_tokenizer,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.tokenizer_name:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.tokenizer_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              raise ValueError(
                  'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
                  'You can do it from another script, save it, and load it from here, using --tokenizer_name.'  )

       if model_args.model_name_or_path:
              __magic_name__      				=    AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path ,     from_tf=bool('.ckpt' in model_args.model_name_or_path  ) ,     config=__snake_case ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     )
       else:
              logger.info('Training new model from scratch'  )
              __magic_name__      				=    AutoModelForMaskedLM.from_config(__snake_case  )

       model.resize_token_embeddings(len(__snake_case  )  )

       # Preprocessing the datasets.
       # First we tokenize all the texts.
       if training_args.do_train:
              __magic_name__      				=    datasets['train'].column_names
       else:
              __magic_name__      				=    datasets['validation'].column_names
       __magic_name__      				=    'text' if 'text' in column_names else column_names[0]

       __magic_name__      				=    'max_length' if data_args.pad_to_max_length else False

       def tokenize_function(__snake_case: str  ):
              # Remove empty lines
              __magic_name__      				=    [line for line in examples['text'] if len(__snake_case  ) > 0 and not line.isspace()]
              return tokenizer(examples['text'] ,     padding=__snake_case ,     truncation=__snake_case ,     max_length=data_args.max_seq_length  )

       __magic_name__      				=    datasets.map(
           __snake_case ,     batched=__snake_case ,     num_proc=data_args.preprocessing_num_workers ,     remove_columns=[text_column_name] ,     load_from_cache_file=not data_args.overwrite_cache ,     )

       # Add the chinese references if provided
       if data_args.train_ref_file is not None:
              __magic_name__      				=    add_chinese_references(tokenized_datasets['train'] ,     data_args.train_ref_file  )
       if data_args.validation_ref_file is not None:
              __magic_name__      				=    add_chinese_references(
                  tokenized_datasets['validation'] ,     data_args.validation_ref_file  )
       # If we have ref files, need to avoid it removed by trainer
       __magic_name__      				=    data_args.train_ref_file or data_args.validation_ref_file
       if has_ref:
              __magic_name__      				=    False

       # Data collator
       # This one will take care of randomly masking the tokens.
       __magic_name__      				=    DataCollatorForWholeWordMask(tokenizer=__snake_case ,     mlm_probability=data_args.mlm_probability  )

       # Initialize our Trainer
       __magic_name__      				=    Trainer(
           model=__snake_case ,     args=__snake_case ,     train_dataset=tokenized_datasets['train'] if training_args.do_train else None ,     eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None ,     tokenizer=__snake_case ,     data_collator=__snake_case ,     )

       # Training
       if training_args.do_train:
              if last_checkpoint is not None:
                     __magic_name__      				=    last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  ):
                     __magic_name__      				=    model_args.model_name_or_path
              else:
                     __magic_name__      				=    None
              __magic_name__      				=    trainer.train(resume_from_checkpoint=__snake_case  )
              trainer.save_model()  # Saves the tokenizer too for easy upload

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'train_results.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Train results *****'  )
                            for key, value in sorted(train_result.metrics.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                     trainer.state.save_to_json(os.path.join(training_args.output_dir ,     'trainer_state.json'  )  )

    # Evaluation
       __magic_name__      				=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'  )

              __magic_name__      				=    trainer.evaluate()

              __magic_name__      				=    math.exp(eval_output['eval_loss']  )
              __magic_name__      				=    perplexity

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'eval_results_mlm_wwm.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Eval results *****'  )
                            for key, value in sorted(results.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

       return results


def 							A						(				__snake_case: List[str]  )    ->		Union[str, Any]:

       """simple docstring"""





       main()


if __name__ == "__main__":
 main()